Bayesian and Non-Bayesian Analysis of the Novel Unit Inverse Exponentiated Lomax Distribution Using Progressive Censoring Schemes with Optimal Scheme and Data Application

Elgarhy, Mohammed; Abdalla, Gaber Sallam Salem; Hassan, Amal Soliman; Almetwally, Ehab M.

doi:10.21608/cjmss.2025.374277.1151

	Bayesian and Non-Bayesian Analysis of the Novel Unit Inverse Exponentiated Lomax Distribution Using Progressive Censoring Schemes with Optimal Scheme and Data Application
Computational Journal of Mathematical and Statistical Sciences
Articles in Press, Accepted Manuscript, Available Online from 11 July 2025 PDF (828.58 K)
Document Type: Original Article
DOI: 10.21608/cjmss.2025.374277.1151
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Authors
Mohammed Elgarhy ¹; Gaber Sallam Salem Abdalla²; Amal Soliman Hassan ³; Ehab M. Almetwally ⁴
¹Department of Basic Sciences, Higher Institute for Administrative Sciences, Belbeis 44621, Egypt
²Department of Insurance and Risk Management, Faculty of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
³Department of Mathematical Statistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
⁴Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh,11432, Saudi Arabia
Abstract
This article presents a novel unit distribution called the unit inverse exponentiated Lomax distribution. Some of its key characteristics are carefully examined. The distribution parameters are estimated using both traditional and Bayesian approaches, taking into account the progressive Type II censoring schemes. Using the symmetric loss function and the Markov chain Monte Carlo technique, the Bayesian methodology is investigated to determine the point and credible interval estimates of parameters. In order to select the best progressive censoring scheme, a number of optimization criteria are taken into consideration. According to the selected criteria measures, the simulations showed that the Bayesian estimates outperform the maximum likelihood estimates in terms of accuracy, indicating better parameter estimation precision. Additionally, most coverage probabilities were high, at about 95%. The novel unit distribution flexibility is validated using a three-real data set from different domains.
Keywords
Inverse exponentiated Lomax distribution; Bayesian estimation; optimal design


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